Measuring Forecasting Skill

Typically assess forecasting skill by examining the squared distance between a forecaster’s forecast and some scoring criterion over many items

\[ X_n = \frac{1}{K} \sum_{k=1}^K (F_{nk} - C_k)^2 \]

Measuring Forecasting Skill

\[ X_n = \frac{1}{K} \sum_{k=1}^K (F_{nk} - C_k)^2 \]

In typical testing, \(C_k\) would be fixed and known

In forecasting, item outcome not known at time of testing

“Correct answer” more like a random variable

Measuring Forecasting Skill

“Correct answer” more like a random variable

E.g., for an hypothetical item \(k\) that is normally distributed with an unknown mean and standard deviation, we can represent this as \[ O_k \sim \mathcal{N}(\mu_k, \sigma_k) \]

An optimal point forecast for this item \(F_k\) would be \(\mu_k\), so we would ideally score \(F_k\) by comparing it to \(\mu_k\)

\[ X_n^{\mu_k} = \frac{1}{K} \sum_{k=1}^K (F_{nk} - \mu_k)^2 \]

Measuring Forecasting Skill

But, since \(\mu_k\) is rarely known, we typically use the outcome \(O_k\) as the scoring criterion instead.

\[X_n^{O_k} = \frac{1}{K} \sum_{k=1}^K (F_{nk} - O_k)^2 \text{ where } O_k \sim \mathcal{N}(\mu_k, \sigma_k)\]

Measurement error is clear if we rewrite this as

\[ X_n^{O_k} = \frac{1}{K} \sum_{k=1}^K (F_{nk} - (\mu_k + \epsilon_{O_k}))^2 \]

, and ideally, we would want to minimize \(\epsilon_{O_k}\).

Intersubjective Measures

Using peer comparisons to score forecasts instead of using outcomes

Wisdom of the crowds: when many predictions are aggregated, individual errors tend to cancel out.

Crowd Aggregates as Scoring Criterion

The logic of wisdom of crowds assumes that the distribution of forecasts for an item has the same mean as the outcome distribution \(\mu_{F_k} = \mu_{k}\).

\[ F_k \sim \mathcal{N} (\mu_k, \sigma_{F_k}) \]

Under the Central Limit Theorem, the average of many forecasts will converge on the average of the outcome distribution with error (inversely) proportional to the sample size of forecasters \(N\)

\[ A_k \sim \mathcal{N} (\mu_k, \frac{\sigma_{F_k}}{\sqrt{N}}) \]

Crowd Aggregates as Scoring Criterion

Substituting the crowd aggregate \(A_k\) for the scoring criterion,

\[X_n^{A_k} = \frac{1}{K} \sum_{k=1}^K (F_{nk} - A_k)^2 \text{ where } A_k \sim \mathcal{N} (\mu_k, \frac{\sigma_{F_k}}{\sqrt{N}}) \]

\[ X_n^{A_k} = \frac{1}{K} \sum_{k=1}^K (F_{nk} - (\mu_k + \epsilon_{A_k}))^2 \]

As long as a crowd is unbiased (\(\mu_{F_k}=\mu_k\)) and sufficiently large \(\left(\frac{\sigma_{F_k}}{\sqrt N}<\sigma_k\right)\), on average \(\epsilon_{A_k}^2<\epsilon_{O_k}^2\) and the wisdom of that crowd \(A_k\) will be a better representation of the optimal point forecast \(\mu_k\) than \(O_k\).

Intersubjective Measures

Using peer comparisons to score forecasts instead of outcomes

Wisdom of the crowds: when many predictions are aggregated, individual errors tend to cancel out.

As long as a crowd is unbiased (\(\mu_{F_k}=\mu_k\)) and sufficiently large \(\left(\frac{\sigma_{F_k}}{\sqrt N}<\sigma_k\right)\), on average \(\epsilon_{A_k}^2<\epsilon_{O_k}^2\) and the wisdom of that crowd \(A_k\) will be a better representation of the optimal point forecast \(\mu_k\) than \(O_k\).

Simulation

Generated forecasts for each forecaster on each item

\(\theta_n\) defined the expected distance between an item’s expected outcome and a forecasters’ forecast

Simulation

Sampled combinations of:

Scored each forecast with:

Simulation: \(\sigma = 1\)

Simulation: \(\sigma = 2\)

Intersubjective Measures

Types of intersubjective measures

If a forecaster correctly identified that \(\mu_k \neq \mu_{F_k}\) (i.e., bias in the crowd), proxy scoring would incorrectly penalize the forecaster for reporting their true belief

Metapredictions explicitly ask about beliefs of \(\mu_{F_k}\) but also require more effort from the forecaster

Intersubjective Measures Existing Work

Tested for their real-time scoring ability but were surprisingly successful at predicting forecasting accuracy

But only explored with categorical forecasting items

Measurement scale confounds reliability boost from intersubjective measures

Present Study

Superforecasters

Previously identified skilled forecasters

Aggregating these superforecasters may serve as a better reference criterion than aggregating the general crowd

Proxy scoring with a superforecaster crowd aggregate has been an effective method

Present Study

1. Are intersubjective measures still good predictors of forecasting accuracy in the (superior reliability) Quantile Elicitation Format (QEF)?

2. Are proxy scores or metapredictions stronger predictors of forecasting accuracy?

Methods

Scoring Methods

Scored each forecast with:

Compared these scores to forecasting accuracy on separate set of thirty items

Aggregate Accuracy

Ground-truth score distributions

Superforecaster aggregate more accurate more often

Correlations

Superforecaster metapredictions and proxy scores strongest

Contributions to Forecasting Proficiency

How much variance in forecaster accuracy does each score explain?

Random effects for item and person, conducted dominance analysis

Select Crowds

Proxy scores effective way of finding select crowds to aggregate

Discussion

Discussion

Intersubjective measures still effective measure of forecasting ability

Forecasting Proficiency Test

Test to measure forecasting proficiency, have data on forecasting accuracy and cognitive tasks

Room for optimization at the item-level?

Optimization Game

Search algorithm for most informative (about forecasting accuracy) items

• Iterates over all items within a set of tasks

• Identifies item that, when added to the “test,” yields the maximum increase in \(R^2\) per testing time

Results: Search Algorithm

Same Pattern, Randomly Selected

Discussion

May be able to decrease testing time for cognitive tasks in the FPT

Some items seem to be adding more noise than predictive ability

Want to identify importance of uniquely informative items vs. randomly selected items from generally informative tasks

Project is very early, interested in feedback and suggestions!

References

Atanasov, P., & Himmelstein, M. (2023). Talent spotting in crowd prediction. In M. Seifert (Ed.), Judgment in Predictive Analytics. Springer.

Atanasov, P., Rescober, P., Stone, E., Swift, S. A., Servan-Schreiber, E., Tetlock, P., Ungar, L., & Mellers, B. (2017). Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls. Management Science, 63(3), 691–706. https://doi.org/10.1287/mnsc.2015.2374

Galton, F. (1907). Vox Populi. Nature, 75(1949), 450–451. https://doi.org/10.1038/075450a0

Himmelstein, M., Budescu, D. V., & Ho, E. H. (2023). The wisdom of many in few: Finding individuals who are as wise as the crowd. Journal of Experimental Psychology: General, 152(5), 1223–1244. https://doi.org/doi.org/10.1037/xge0001340

Himmelstein, M., Zhu, S. M., Petrov, N., Karger, E., Helmer, J., Livnat, S., Bennett, A., Hedley, P., & Tetlock, P. (2025). The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability. OSF. https://doi.org/10.31234/osf.io/a7kdx

Karger, E., Monrad, J., Mellers, B., & Tetlock, P. (2021). Reciprocal Scoring: A Method for Forecasting Unanswerable Questions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3954498

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.

Wilkening, T., Martinie, M., & Howe, P. D. L. (2022). Hidden Experts in the Crowd: Using Meta-Predictions to Leverage Expertise in Single-Question Prediction Problems. Management Science, 68(1), 487–508. https://doi.org/10.1287/mnsc.2020.3919

Zhu, S. M., Budescu, D. V., Petrov, N., Karger, E., & Himmelstein, M. (2024). The psychometric properties of probability and quantile forecasts. Preprint.